Opioid Overdoses Among High-Risk Medicaid Members: Healthcare Cost, Service Utilization, and Risk Factor Analysis

Research Objective: Identify risk factors associated with opioid overdoses among three high-risk populations of Medicaid members related to cost and service utilization. Study Design: Repeated cross-sectional study using five years of Massachusetts Medicaid (MassHealth) claims and state agency data. Population Studied: MassHealth members aged 11-64 years considered to be high-risk (homeless, unstably housed, and/or criminal justice-involved) and in need of support services, especially those with extensive behavioral health (BH) needs. These three populations were identified as being particularly vulnerable to non-fatal and/or fatal opioid overdoses. Principal Findings: MassHealth members who were both justice-involved and unstably housed were at much higher risk of an opioid overdose than the MassHealth population overall, especially those with a substance use disorder (SUD) or a serious mental illness (SMI). Experiencing both homelessness and justice involvement substantially compounded members’ non-fatal overdose risk, regardless of BH diagnosis. Co-occurring SUD/SMI was a key driver of high overdose prevalence, particularly among the justice-involved. Compared to MassHealth members in general, those with justice involvement and unstable housing had costs that were 50-65% higher; members who experienced homelessness had triple the costs. Healthcare service use both before and after an overdose was relatively low, including the timeframe between multiple non-fatal overdoses. In multivariate analyses, all three high-risk factors (i.e., populations) were significantly related to an increased opioid overdose risk after controlling for additional risk factors (BH diagnoses, chronic medical conditions, and demographic characteristics). Males and whites were more likely to have an opioid overdose; those with diabetes or hypertension were less likely. These results were similar when assessing various opioid overdose outcomes (non-fatal and/or fatal). Conclusions: These findings helped inform MassHealth’s understanding of its members’ experiences regarding medical and BH services, especially among high-risk populations with an opioid overdose. The identification of risk factors most predictive of a subsequent overdose may help address the needs of these high-risk groups. For most of the populations studied, prevalence of co-occurring BH diagnoses was much higher than MassHealth members in general and appeared to impact opioid overdose rates. Most members received services for 1-2 months in both the pre- and post-overdose periods; service use was relatively low in the year following a non-fatal overdose, suggesting retention was also low. Multivariate analyses consistently showed that gender and race were significantly associated with increased overdose risk. Implications for Policy or Practice: Understanding opioid overdose risk factors and identifying service utilization gaps and missed opportunities are important. As payment reforms evolve under the umbrella of accountable care organizations, BH community partnership models are key for collaborating with healthcare and social service providers, and community resources for care management, care coordination, and referrals to support services. Our study initially developed an in-depth descriptive analysis of individuals with SUD, SMI, or both identified as being at high risk for an opioid overdose. Understanding service trajectory and outcomes through additional analyses was critical for planning and prioritizing appropriate services. As payors are actively making decisions about effective systems of care, they are particularly interested in understanding the need for community-based and residential services, particularly for those with housing instability and/or criminal justice involvement

Small area Forecasting of Opioid-Related Mortality: Bayesian Spatiotemporal Dynamic Modeling approach

BACKGROUND: Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation. OBJECTIVE: The overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts. METHODS: We obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts\u27 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas. RESULTS: Bayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019. CONCLUSIONS: Our Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy

A classification model of homelessness using integrated administrative data: Implications for targeting interventions to improve the housing status, health and well-being of a highly vulnerable population

Homelessness is poorly captured in most administrative data sets making it difficult to understand how, when, and where this population can be better served. This study sought to develop and validate a classification model of homelessness. Our sample included 5,050,639 individuals aged 11 years and older who were included in a linked dataset of administrative records from multiple state-maintained databases in Massachusetts for the period from 2011-2015. We used logistic regression to develop a classification model with 94 predictors and subsequently tested its performance. The model had high specificity (95.4%), moderate sensitivity (77.8%) for predicting known cases of homelessness, and excellent classification properties (area under the receiver operating curve 0.94; balanced accuracy 86.4%). To demonstrate the potential opportunity that exists for using such a modeling approach to target interventions to mitigate the risk of an adverse health outcome, we also estimated the association between model predicted homeless status and fatal opioid overdoses, finding that model predicted homeless status was associated with a nearly 23-fold increase in the risk of fatal opioid overdose. This study provides a novel approach for identifying homelessness using integrated administrative data. The strong performance of our model underscores the potential value of linking data from multiple service systems to improve the identification of housing instability and to assist government in developing programs that seek to improve health and other outcomes for homeless individuals

Opioid overdose deaths and potentially inappropriate opioid prescribing practices (PIP): A spatial epidemiological study

INTRODUCTION: Opioid overdose deaths quintupled in Massachusetts between 2000 and 2016. Potentially inappropriate opioid prescribing practices (PIP) are associated with increases in overdoses. The purpose of this study was to conduct spatial epidemiological analyses of novel comprehensively linked data to identify overdose and PIP hotspots. METHODS: Sixteen administrative datasets, including prescription monitoring, medical claims, vital statistics, and medical examiner data, covering \u3e98% of Massachusetts residents between 2011-2015, were linked in 2017 to better investigate the opioid epidemic. PIP was defined by six measures: \u3e /=100 morphine milligram equivalents (MMEs), co-prescription of benzodiazepines and opioids, cash purchases of opioid prescriptions, opioid prescriptions without a recorded pain diagnosis, and opioid prescriptions through multiple prescribers or pharmacies. Using spatial autocorrelation and cluster analyses, overdose and PIP hotspots were identified among 538 ZIP codes. RESULTS: More than half of the adult population (n = 3,143,817, ages 18 and older) were prescribed opioids. Nearly all ZIP codes showed increasing rates of overdose over time. Overdose clusters were identified in Worcester, Northampton, Lee/Tyringham, Wareham/Bourne, Lynn, and Revere/Chelsea (Getis-Ord Gi*; p \u3c 0.05). Large PIP clusters for \u3e /=100 MMEs and prescription without pain diagnosis were identified in Western Massachusetts; and smaller clusters for multiple prescribers in Nantucket, Berkshire, and Hampden Counties (p \u3c 0.05). Co-prescriptions and cash payment clusters were localized and nearly identical (p \u3c 0.05). Overlap in PIP and overdose clusters was identified in Cape Cod and Berkshire County. However, we also found contradictory patterns in overdose and PIP hotspots. CONCLUSIONS: Overdose and PIP hotspots were identified, as well as regions where the two overlapped, and where they diverged. Results indicate that PIP clustering alone does not explain overdose clustering patterns. Our findings can inform public health policy decisions at the local level, which include a focus on PIP and misuse of heroin and fentanyl that aim to curb opioid overdoses

One-year mortality of patients after emergency department treatment for nonfatal opioid overdose.

STUDY OBJECTIVE: Despite the increased availability of naloxone, death rates from opioid overdose continue to increase. The goal of this study is to determine the 1-year mortality of patients who were treated for a nonfatal opioid overdose in Massachusetts emergency departments (EDs). METHODS: This was a retrospective observational study of patients from 3 linked statewide Massachusetts data sets: a master demographics list, an acute care hospital case-mix database, and death records. Patients discharged from the ED with a final diagnosis of opioid overdose were included. The primary outcome measure was death from any cause within 1 year of overdose treatment. RESULTS; During the study period, 17,241 patients were treated for opioid overdose. Of the 11,557 patients who met study criteria, 635 (5.5%) died within 1 year, 130 (1.1%) died within 1 month, and 29 (0.25%) died within 2 days. Of the 635 deaths at 1 year, 130 (20.5%) occurred within 1 month and 29 (4.6%) occurred within 2 days. CONCLUSION; The short-term and 1-year mortality of patients treated in the ED for nonfatal opioid overdose is high. The first month, and particularly the first 2 days after overdose, is the highest-risk period. Patients who survive opioid overdose should be considered high risk and receive interventions such as being offered buprenorphine, counseling, and referral to treatment before ED discharge

Overdose risk for veterans receiving opioids from multiple sources

OBJECTIVES: The aim of this study was to evaluate whether veterans in Massachusetts receiving opioids and/or benzodiazepines from both Veterans Health Administration (VHA) and non-VHA pharmacies are at higher risk of adverse events compared with those receiving opioids at VHA pharmacies only. STUDY DESIGN: A cohort study of veterans who filled a prescription for any Schedule II through V substance at a Massachusetts VHA pharmacy. Prescriptions were recorded in the Massachusetts Department of Public Health Chapter 55 data set. METHODS: The study sample included 16,866 veterans residing in Massachusetts, of whom 9238 (54.8%) received controlled substances from VHA pharmacies only and 7628 (45.2%) had filled prescriptions at both VHA and non-VHA pharmacies ( dual care users ) between October 1, 2013, and December 31, 2015. Our primary outcomes were nonfatal opioid overdose, fatal opioid overdose, and all-cause mortality. RESULTS: Compared with VHA-only users, more dual care users resided in rural areas (12.6% vs 10.6%), received high-dose opioid therapy (26.3% vs 7.3%), had concurrent prescriptions of opioids and benzodiazepines (34.8% vs 8.2%), and had opioid use disorder (6.8% vs 1.6%) (P \u3c .0001 for all). In adjusted models, dual care users had higher odds of nonfatal opioid overdose (odds ratio [OR], 1.29; 95% CI, 0.98-1.71) and all-cause mortality (OR, 1.66; 95% CI, 1.43-1.93) compared with VHA-only users. Dual care use was not associated with fatal opioid overdoses. CONCLUSIONS: Among veterans in Massachusetts, receipt of opioids from multiple sources was associated with worse outcomes, specifically nonfatal opioid overdose and mortality. Better information sharing between VHA and non-VHA pharmacies and prescribers has the potential to improve patient safety

Massachusetts Prevalence of Opioid Use Disorder Estimation Revisited: Comparing a Bayesian Approach to Standard Capture–Recapture Methods

Background: The National Survey on Drug Use and Health (NSDUH) estimated the prevalence of opioid use disorder (OUD) among the civilian, noninstitutionalized people aged 12 years or older in Massachusetts as 1.2% between 2015 and 2017. Accurate estimation of the prevalence of OUD is critical to the success of treatment and resource planning. Various indirect estimation approaches have been used but are subject to data availability and infrastructure-related issues. Methods: We used 2015 data from the Massachusetts Public Health Data Warehouse (PHD) to compare the results of two approaches to estimating OUD prevalence in the Massachusetts population. First, we used a seven-dataset capture–recapture analysis under log–linear model parameterization, controlling for the source dependence and effects of age, sex, and county through stratification. Second, we applied a benchmark-multiplier method in a Bayesian framework by linking health care claims data to death certificate data assuming an extrapolation of death rates from observed untreated OUD to unobserved OUD. Results: Our estimates for OUD prevalence among Massachusetts residents (aged 18–64 years) were 4.62% (95% CI = 4.59%, 4.64%) in the capture–recapture approach and 4.29% (95% CrI = 3.49%, 5.32%) in the Bayesian model. Both estimates were approximately four times higher than NSDUH estimates. Conclusion: The synthesis of our findings suggests that the disease surveillance system misses a large portion of the population with OUD. Our study also suggests that concurrent use of multiple methods improves the justification and facilitates the triangulation and interpretation of the resulting estimates. Trial registration: ClinicalTrials.gov Identifier: NCT04111939

Non-fatal opioid-related overdoses among adolescents in Massachusetts 2012-2014

BACKGROUND: Opioid-related overdoses and deaths among adolescents in the United States continue to increase, but little is known about adolescents who experience opioid-related non-fatal overdose (NFOD). Our objective was to describe (1) the characteristics of adolescents aged 11-17 who experienced NFOD and (2) their receipt of medications for opioid use disorder (MOUD) in the 12 months following NFOD, compared with adults. METHODS: We created a retrospective cohort using six Massachusetts state agency datasets linked at the individual level, with information on 98% of state residents. Individuals entered the cohort if they experienced NFOD between January 1, 2012 and December 31, 2014. We compared adolescents to adults experiencing NFOD, examining individual characteristics and receipt of medications for opioid use disorder (MOUD)-methadone, buprenorphine, or naltrexone. RESULTS: Among 22,506 individuals who experienced NFOD during the study period, 195 (0.9%) were aged 11-17. Fifty-two percent (102/195) of adolescents were female, whereas only 38% of adults were female (P \u3c 0.001). In the year prior to NFOD, 11% (21/195) of adolescents received a prescription opioid, compared to 43% of adults (P \u3c 0.001), and \u3c 5% ( \u3c 10/195) received any MOUD compared to 23% of adults (P \u3c 0.001). In the 12 months after NFOD, only 8% (15/195) of adolescents received MOUD, compared to 29% of adults. CONCLUSION: Among individuals experiencing NFOD, adolescents were more likely to be female and less likely to have been prescribed opioids in the year prior. Few adolescents received MOUD before or after NFOD. Non-fatal overdose is a missed opportunity for starting evidence-based treatment in adolescents